code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
AutoencoderKL,
DDIM... | 551 |
from unittest.mock import Mock, patch
from file_transfer.send_file import send_file
@patch("socket.socket" )
@patch("builtins.open" )
def UpperCamelCase_( _A :Tuple , _A :str )-> int:
# ===== initialization =====
UpperCamelCase__ = Mock()
UpperCamelCase__ = conn, Mo... | 551 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__:Tuple = {
"""configuration_resnet""": ["""RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", "... | 67 | """simple docstring"""
from __future__ import annotations
def _lowerCamelCase( a , a , a ):
if len(a ) == 0:
raise ValueError("find_max() arg is an empty sequence" )
if (
left >= len(a )
or left < -len(a )
... | 67 | 1 |
# Lint as: python3
import itertools
import os
import re
_lowercase : int =re.compile(r"""([A-Z]+)([A-Z][a-z])""")
_lowercase : int =re.compile(r"""([a-z\d])([A-Z])""")
_lowercase : List[str] =re.compile(r"""(?<!_)_(?!_)""")
_lowercase : str =re.compil... | 364 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowercase : int = {
'configuration_conditional_detr': [
'CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP',
'ConditionalDetrConfig',
... | 557 | 0 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
class lowerCamelCase_ ( snake_case_ ):
_lowerCAmelCase : Optional[int] = 'WhisperFeatureExtractor'
_lowerCAmelCase : Optional[Any] = 'WhisperTokenizer'
def __... | 464 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCAmelCase_ : Tuple = {
'configuration_tapas': ['TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TapasConfig'],
... | 464 | 1 |
"""simple docstring"""
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
... | 673 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.tex... | 21 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_... | 705 |
'''simple docstring'''
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def __A ( _SCREAMING_SNAKE_CASE : np.ndarray , _SCREAMING_SNAKE_CASE : np.ndarray ):
"""simple docstring"""
... | 564 | 0 |
def _A ( __snake_case :list[int] ) -> float:
"""simple docstring"""
if not nums: # Makes sure that the list is not empty
raise ValueError("List is empty" )
__SCREAMING_SNAKE_CASE = sum(__snake_case ) / len(__snake_case ) # Calculate the average
retu... | 693 |
import random
from .binary_exp_mod import bin_exp_mod
def _A ( __snake_case :List[Any] , __snake_case :Union[str, Any]=1000 ) -> int:
"""simple docstring"""
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this means n is odd
__... | 693 | 1 |
'''simple docstring'''
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union
from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings
_... | 270 |
'''simple docstring'''
__UpperCamelCase : Tuple = """0.18.2"""
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffu... | 270 | 1 |
from ..utils import DummyObject, requires_backends
class _a ( metaclass=__snake_case ):
"""simple docstring"""
A_ = ["""sentencepiece"""]
def __init__( self , *_UpperCAmelCase , **_UpperCAmelCase ) -> List[Any]:
requ... | 23 |
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
ConditionalDetrConfig,
ConditionalDetrForObjectDetection,
ConditionalDetrForSe... | 16 | 0 |
'''simple docstring'''
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
... | 464 |
'''simple docstring'''
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils ... | 464 | 1 |
import warnings
from contextlib import contextmanager
from ....processing_utils import ProcessorMixin
class _snake_case ( _snake_case ):
SCREAMING_SNAKE_CASE__ = 'MCTCTFeatureExtractor'
SCREAMING_SNAKE_CASE__ = 'AutoTokenizer'
def __init__( self , _lowerCamelCase , _lowe... | 445 |
import json
import os
import shutil
import tempfile
import unittest
from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property
from ...test_tok... | 445 | 1 |
import inspect
import unittest
from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_com... | 157 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
UniSpeechConfig,
UniSpeechForCTC,
UniSpeechForPreTraining,
WavaVecaFeatureExtractor,
WavaVecaPhonemeCTCTokenizer,
WavaVecaProcessor,
l... | 157 | 1 |
'''simple docstring'''
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> An... | 591 |
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..models.auto.modeli... | 194 | 0 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE_ = {"""configuration_focalnet""": ["""FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FocalNetConfig"""]}
try:
if not is_torch_av... | 719 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class snake_case_ ( unittest.TestC... | 370 | 0 |
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_torch_available():
import torch
from ..models... | 524 | import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
from transformers import AutoTokenizer, FlaxMTaForCo... | 524 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__A : List[str] = {
'configuration_resnet': ['RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ResNe... | 717 |
'''simple docstring'''
import tempfile
import unittest
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
@require_torch
@require_optim... | 267 | 0 |
'''simple docstring'''
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def __magic_name__ ( __UpperCAmelCase , __UpperC... | 109 |
"""simple docstring"""
UpperCamelCase__ :Tuple = frozenset(
[
"""prompt""",
"""height""",
"""width""",
"""guidance_scale""",
"""negative_prompt""",
"""prompt_embeds""",
"""negative_prompt_embeds""",
"""cross_attention_kwargs""",
]
... | 355 | 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer
from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.model... | 39 |
'''simple docstring'''
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms
from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IM... | 39 | 1 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable
__UpperCAmelCase = {
'''configuration_gpt_neox_japanese''': ['''GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoXJapaneseConfig'''],
'''tokeniz... | 40 |
def _SCREAMING_SNAKE_CASE ( lowerCAmelCase__ ,lowerCAmelCase__ ):
if number < 0 or shift_amount < 0:
raise ValueError('both inputs must be positive integers' )
lowerCamelCase_ : int = str(bin(lowerCAmelCase__ ) )
binary_number += "0" * shift_amo... | 364 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
SCREAMING_SNAKE_CASE :... | 714 |
"""simple docstring"""
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Un... | 229 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
a_ = {
'configuration_convnext': ['CONVNEXT_PRETRAINED_CONFIG_ARC... | 76 |
"""simple docstring"""
import argparse
import os
import re
import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints
from transformers import (
AutoTokenizer,
PixaStructConfig,
PixaStructForConditionalGeneration,
PixaStructImageProcessor,
PixaStructProcessor,
... | 499 | 0 |
'''simple docstring'''
from __future__ import annotations
def UpperCamelCase ( a , a , a ) -> float:
'''simple docstring'''
if days_between_payments <= 0:
raise ValueError('''days_between_payments must be > 0''' )
if daily_interest_rate < 0:
raise ... | 710 |
'''simple docstring'''
_lowerCAmelCase = {
"Pillow": "Pillow",
"accelerate": "accelerate>=0.11.0",
"compel": "compel==0.1.8",
"black": "black~=23.1",
"datasets": "datasets",
"filelock": "filelock",
"flax": "flax>=0.4.1",
"hf-doc-builder": "hf-doc-builder>=0.3.0",
"hugg... | 245 | 0 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org... | 52 |
'''simple docstring'''
import os
from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home
_UpperCamelCase : Any =HUGGINGFACE_HUB_CACHE
_UpperCamelCase : List[str] ="config.json"
_UpperCamelCase : Union[str, Any] ="diffusion_pytorch_model.bin"
_UpperC... | 316 | 0 |
from argparse import ArgumentParser, Namespace
from typing import Any, List, Optional
from ..pipelines import Pipeline, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from fastapi import Body, FastAPI, HTTPException
from fastapi.routing import APIRout... | 701 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder imp... | 607 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__A = {
"""configuration_mvp""": ["""MVP_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MvpConfig""", """MvpOnnxConfig"""],
"""... | 93 |
'''simple docstring'''
from __future__ import annotations
import numpy as np
def UpperCamelCase__ ( lowerCAmelCase ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = np.shape(lowerCAmelCase )
if rows != columns:
... | 207 | 0 |
"""simple docstring"""
import os
def UpperCAmelCase ( ):
'''simple docstring'''
_UpperCAmelCase = os.path.dirname(os.path.realpath(A ) )
_UpperCAmelCase = os.path.join(A , 'triangle.txt' )
with open(A ) as f:
_Uppe... | 24 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase = {
'''configuration_mvp''': ['''MVP_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MvpConfig''', '''MvpOnnxCo... | 24 | 1 |
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ftp_get,
ft... | 97 |
def lowerCAmelCase__( lowercase : list ) -> list:
__snake_case : str = len(lowercase )
for _ in range(lowercase ):
for i in range(_ % 2 , arr_size - 1 , 2 ):
if arr[i + 1] < arr[i]:
__snake_case , __snake_case : str ... | 243 | 0 |
from __future__ import annotations
import math
def A_ ( snake_case ):
if num <= 0:
SCREAMING_SNAKE_CASE:Any = F'''{num}: Invalid input, please enter a positive integer.'''
raise ValueError(snake_case__ )
SCREAMING_SNAKE_CASE:List[str] = [True] * (n... | 700 |
'''simple docstring'''
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
A_ = {
"facebook/mask2former-swin-small-coco-instance": (
"https://huggingface.c... | 465 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaInpaintPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, l... | 334 | import unittest
from transformers import BigBirdConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax
from transformers.models.big_bird.modelin... | 321 | 0 |
'''simple docstring'''
def __snake_case ( lowercase : int ):
snake_case_ = [[0 for _ in range(lowercase )] for _ in range(m + 1 )]
for i in range(m + 1 ):
snake_case_ = 1
for n in range(m + 1 ):
for k in range(1 , lowercase... | 420 |
'''simple docstring'''
from __future__ import annotations
from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray
def __snake_case ( lowercase : float , lowercase : float , lowercase : bool = False ):
if radian_mode... | 420 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase__ = {'''configuration_focalnet''': ['''FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FocalNetCo... | 83 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
lowerCAmelCase__ = TypeVar('''T''')
class __snake_case ( Generic[T]):
def __init__( self : int , __lowerCAmelCase : T ):
... | 83 | 1 |
"""simple docstring"""
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device
if is_torch_available():
from transformers import PyTorchBenchmark, PyTorchB... | 713 |
"""simple docstring"""
import functools
def lowercase ( _SCREAMING_SNAKE_CASE : list[int] , _SCREAMING_SNAKE_CASE : list[int] ):
'''simple docstring'''
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) or n... | 95 | 0 |
"""simple docstring"""
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
SCREAMING_SNAKE_CASE_ = datasets.utils.logging.get_logger(__name__)
class snake_case_ ( folder_based_builder.FolderB... | 34 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
"facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/config.json",
# See all XGLM models at https://huggin... | 346 | 0 |
import re
def UpperCAmelCase_( a__ ):
"""simple docstring"""
if len(re.findall('''[ATCG]''' , a__ ) ) != len(a__ ):
raise ValueError('''Invalid Strand''' )
return dna.translate(dna.maketrans('''ATCG''' , '''TAGC''' ) )
if __name__ =... | 333 |
from numpy import exp, pi, sqrt
def UpperCAmelCase_( a__ , a__ = 0.0 , a__ = 1.0 ):
"""simple docstring"""
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 333 | 1 |
import numpy as np
# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models
if __name__ == "__main__":
# Initialising the CNN
# (Sequential- Building the model layer by layer)
snake_case__ : str = models.Sequential()
... | 408 |
import json
import os
import re
import unicodedata
from json.encoder import INFINITY
from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
import regex
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding
from ...utils import T... | 408 | 1 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_avai... | 707 |
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
if version.parse(fairseq._... | 437 | 0 |
from __future__ import annotations
import typing
from collections import Counter
def __a ( lowerCAmelCase_ : int ) -> typing.Counter[int]:
'''simple docstring'''
UpperCAmelCase_= Counter()
for base in range(1 ,max_perimeter + 1 ):
for perpe... | 593 |
import copy
from collections import OrderedDict
from typing import Dict, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
__A = logging.get_logger(__name__)
__A ... | 593 | 1 |
"""simple docstring"""
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from... | 295 |
"""simple docstring"""
def _lowerCAmelCase ( lowerCamelCase__ : str, lowerCamelCase__ : str ) -> Union[str, Any]:
print("\nThe shortest path matrix using Floyd Warshall algorithm\n" )
for i in range(lowerCamelCase__ ):
for j in range(lowerCamelCase__ ... | 295 | 1 |
'''simple docstring'''
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__ : List[str] = logging.get_logger(__name__)
# TODO Update this
A__ : Tuple = {
"""facebook/esm-1b""": "... | 13 |
'''simple docstring'''
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def _UpperCAmelCase ( _lowerCamelCase : int ) -> int:
_lowerCAmelCase : Any = prime_factors(_lowerCamelCase )
if is_square_free(_lowerCamelCase ):
... | 384 | 0 |
from ..utils import DummyObject, requires_backends
class SCREAMING_SNAKE_CASE_ ( metaclass=__lowercase ):
'''simple docstring'''
lowercase : str = ["onnx"]
def __init__( self : Optional[int] , *SCREAMING_SNAKE_CASE__ : Union[str, Any] ... | 706 | from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class SCRE... | 661 | 0 |
def lowerCamelCase__ ( _lowercase ):
'''simple docstring'''
UpperCAmelCase_ : List[str] = len(_lowercase )
for _ in range(_lowercase ):
for i in range(_ % 2 , arr_size - 1 , 2 ):
if arr[i + 1] < arr[i]:
UpperCAmelCase_, ... | 30 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_UpperCAmelCase : List[Any] = {
'''configuration_layoutlmv2''': ['''LAYOUT... | 107 | 0 |
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
_lowercase = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))
import check_copies # noqa: E402
# This is the reference code that... | 704 |
'''simple docstring'''
import itertools
import json
import os
import unittest
from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_t... | 427 | 0 |
import os
from collections import deque
import torch
from torch.utils.data import Dataset
class snake_case ( SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
def __init__( self : Any , lowerCAmelCase : Any="" , lowerCAmelCase... | 477 |
from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
LineByLineTextDataset,
LineByLineWithRefDataset,
LineByLineWithSOPTextDataset,
TextDataset,
TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments
| 477 | 1 |
'''simple docstring'''
import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch
from parameterized import parameterized
from transformers.testing_utils import (
CaptureStderr,
ExtendSysPath,
TestCasePlus,
execute_s... | 0 |
'''simple docstring'''
from __future__ import annotations
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Tuple:
"""simple docstring"""
print(F"Vertex\tShortest Distance from vertex {src}" )
for i, d in enumerate(SCREAMING_SNAKE_CASE_ ):
... | 0 | 1 |
from __future__ import annotations
from typing import Any
class __lowercase :
"""simple docstring"""
def __init__( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = 0 ) -> None:
A , A : ... | 542 |
def snake_case__ ( lowerCamelCase_ = 1000 ):
return sum(e for e in range(3 , lowerCamelCase_ ) if e % 3 == 0 or e % 5 == 0 )
if __name__ == "__main__":
print(F"{solution() = }")
| 542 | 1 |
from ...processing_utils import ProcessorMixin
class _lowercase ( A__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[str] = '''SpeechT5FeatureExtractor'''
SCREAMING_SNAKE_CASE__ : Optional[int] = '''SpeechT5Tokenizer'''
def __init__( self ... | 260 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowerCAmelCase : Optional[Any] ={'configuration_focalnet': ['FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FocalNetConfig']}
try:
if not... | 260 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import to... | 108 | def UpperCAmelCase_ ( _UpperCAmelCase ):
lowerCamelCase_: Any = current_set.copy()
for row_index, row in enumerate(_UpperCAmelCase ):
lowerCamelCase_: Optional[Any] = row[0]
for column_index, column in enumerate(_UpperCAmelCase ):
... | 423 | 0 |
"""simple docstring"""
from math import factorial
def __lowerCamelCase ( SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE ) -> float:
"""simple docstring"""
if successes > trials:
raise ValueError('successes must be lower or eq... | 712 |
"""simple docstring"""
from __future__ import annotations
import os
import tempfile
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_text, require... | 494 | 0 |
"""simple docstring"""
def a__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> int:
return int((input_a, input_a).count(0 ) == 0 )
def a__ ( ) -> None:
assert and_gate(0 , 0 ) == 0
assert and_gate... | 346 |
"""simple docstring"""
import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from cookiecutter.main import cookiecutter
... | 346 | 1 |
'''simple docstring'''
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
lowercase : str = TypeVar('T')
def lowerCAmelCase_ ( snake_case__ ):
'''simple docstring'''
return (position - 1) //... | 343 |
'''simple docstring'''
import argparse
import json
import numpy
import torch
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def lowerCAmelCase_ ( snak... | 343 | 1 |
import unittest
from transformers import (
MODEL_FOR_OBJECT_DETECTION_MAPPING,
AutoFeatureExtractor,
AutoModelForObjectDetection,
ObjectDetectionPipeline,
is_vision_available,
pipeline,
)
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
requ... | 557 |
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, r... | 557 | 1 |
'''simple docstring'''
class __UpperCAmelCase :
'''simple docstring'''
def __init__( self , _SCREAMING_SNAKE_CASE ) -> None:
A_ = len(_SCREAMING_SNAKE_CASE )
A_ = [0] * len_array
if len_array > 0:
... | 174 | '''simple docstring'''
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
__snake_case : Union[str, Any] = '\\n@article{wang2019superglue,\n title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Under... | 174 | 1 |
'''simple docstring'''
from itertools import permutations
def UpperCamelCase__ ( _lowercase : tuple ) -> bool:
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
__UpperCAmelCase: ... | 523 | '''simple docstring'''
def UpperCamelCase__ ( _lowercase : list ) -> list:
if len(_lowercase ) < 2:
return collection
def circle_sort_util(_lowercase : list , _lowercase : int , _lowercase : int ) -> bool:
__UpperCAmelCase: Tupl... | 523 | 1 |
"""simple docstring"""
from __future__ import annotations
from math import pi
# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
_lowerCAmelCase = 1.054_571_817E-34 # unit of ℏ : J * s
_lowerCAmelCase = 3E8 # unit of c : m * s^-1
... | 701 |
"""simple docstring"""
import json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = {
"""vocab_file""": """vocab.j... | 16 | 0 |
'''simple docstring'''
def __lowerCamelCase ( ) ->List[str]:
return [list(range(10_00 - i , -10_00 - i , -1 ) ) for i in range(10_00 )]
a__ : Any = generate_large_matrix()
a__ : Optional[Any] = (
[[4, 3, 2,... | 368 |
'''simple docstring'''
def __snake_case ( ):
lowerCamelCase_ = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
lowerCamelCase_ = 6
lowerCamelCase_ = 1
lowerCamelCase_ = 1901
lowerCamelCase_ = 0
while year < 2001:
day += 7
if (year % 4 == 0 an... | 675 | 0 |
'''simple docstring'''
import os
import unittest
from transformers import MobileBertTokenizer, MobileBertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transfo... | 0 |
'''simple docstring'''
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def lowerCAmelCase_ ( ) -> List[Any]:
"""simple docstring"""
with offline(Offli... | 0 | 1 |
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
nested_simplif... | 23 | '''simple docstring'''
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def lowerCAmelCase ( UpperCamelCase__ : BertModel , UpperCamelCase__ : str , UpperCamelCase__ : str ):
... | 262 | 0 |
def lowerCAmelCase_ ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ):
__snake_case : Dict = len(__lowerCamelCase )
__snake_case : Optional[int] = [[0] * n for i in range(__lowerCamelCase )]
for i in range(__lowerCamel... | 203 |
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
_snake_case : List[Any] = ... | 203 | 1 |
import math
import sys
import cva
import numpy as np
def _SCREAMING_SNAKE_CASE ( lowercase : int , lowercase : str ):
'''simple docstring'''
lowerCamelCase_ = math.sqrt(lowerCamelCase__ )
lowerCamelCase_ = 1 / (si... | 70 |
import math
class snake_case__:
"""simple docstring"""
def __init__( self : int , SCREAMING_SNAKE_CASE : List[Any]=0 ): # a graph with Node 0,1,...,N-1
lowercase__ : Dict = n
lowercase__ : List[Any] = [
[math.inf for j in ran... | 496 | 0 |
import warnings
from ...utils import is_sklearn_available, requires_backends
if is_sklearn_available():
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
lowerCAmelCase__ = (
"This metric will be removed from the library soon, metrics should... | 1 |
import numpy
# List of input, output pairs
lowerCAmelCase__ = (
((5, 2, 3), 15),
((6, 5, 9), 25),
((11, 12, 13), 41),
((1, 1, 1), 8),
((11, 12, 13), 41),
)
lowerCAmelCase__ = (((515, 22, 13), 555), ((61, 35, 49), 150))
lowerCAmelCase__ = [2, 4, 1, 5]
lowerCAmelCase__ = len... | 1 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE : Any = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE ... | 294 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_b... | 294 | 1 |
import os
def lowercase ( _a = "matrix.txt" ) -> int:
with open(os.path.join(os.path.dirname(_a ) ,_a ) ) as in_file:
UpperCAmelCase_: str = in_file.read()
UpperCAmelCase_: Union[str, Any] = [[int(_a ) for cell in row.split("," )] for row in d... | 306 |
def lowercase ( _a = 2000000 ) -> int:
UpperCAmelCase_: List[str] = [0 for i in range(n + 1 )]
UpperCAmelCase_: str = 1
UpperCAmelCase_: Union[str, Any] = 1
for i in range(2 ,int(n**0.5 ) + 1 ):
if primality_list[i] == 0:
for j in ... | 306 | 1 |
"""simple docstring"""
from __future__ import annotations
def a_ ( __a , __a ):
A__ = 0
A__ = len(__a ) - 1
while i < j:
if nums[i] + nums[j] == target:
return [i, j]
elif nums[i] + nums[j] <... | 571 |
UpperCamelCase = 256
# Modulus to hash a string
UpperCamelCase = 100_0003
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
A_ : Any = len(SCREAMING_SNAKE_CASE )
A_ : int = len(SCREAMING_SNAKE_CASE... | 590 | 0 |
"""simple docstring"""
import copy
from collections import OrderedDict
from typing import Dict, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
UpperCA... | 708 |
"""simple docstring"""
import math
import os
import sys
def lowercase_ ( _snake_case ):
SCREAMING_SNAKE_CASE__ : List[str] = """"""
try:
with open(_snake_case ,"""rb""" ) as binary_file:
SCREAMING_SNAKE_CASE__ : str = ... | 545 | 0 |
from __future__ import annotations
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available
from ...test_modeling_tf_common import floats_tensor, ids_... | 282 |
def __lowerCamelCase ( _lowercase ) -> list:
UpperCamelCase = len(_lowercase )
for _ in range(_lowercase ):
for i in range(_ % 2 , arr_size - 1 , 2 ):
if arr[i + 1] < arr[i]:
UpperCamelCase , UpperCa... | 282 | 1 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Sequence, Value
from .base import TaskTemplate
@dataclass(frozen=UpperCamelCase)
class snake_case__ ( UpperCamelCase):
# `task` is not a ClassVar since we want i... | 216 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
_UpperCamelCase : Optional[int] = {
'configuration_longt5': ['LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LongT5Config', 'LongT5OnnxConfi... | 216 | 1 |
"""simple docstring"""
from collections import deque
from math import floor
from random import random
from time import time
class lowerCAmelCase__ :
def __init__( self ):
'''simple docstring'''
A__ = {}
def lowercase_ ( self , UpperCamelCase__ ... | 337 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCAmelCase ={
"""configuration_rembert""": ["""REMBER... | 337 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__SCREAMING_SNAKE_CASE : int = {'''configuration_reformer''': ['''REFORMER_PRETRAINED_CONFIG_ARCHIVE_... | 149 |
import json
import os
import re
import sys
import urllib.request
import requests
from bsa import BeautifulSoup
__SCREAMING_SNAKE_CASE : Dict = {
'''User-Agent''': '''Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'''
''' (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 E... | 149 | 1 |
import argparse
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
CLIPTokenizer,
CLIPTokenizerFast,
VideoMAEImageProcessor,
XCLIPConfig,
XCLIPModel,
XCLIPProcessor,
XCLIPTextConfig,
XCLIPVisionConfig,
)
def ... | 484 |
import argparse
import torch
from transformers import (
EncodecConfig,
EncodecFeatureExtractor,
EncodecModel,
logging,
)
# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/compressi... | 484 | 1 |
'''simple docstring'''
import json
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common impo... | 712 |
'''simple docstring'''
from collections.abc import Sequence
def UpperCAmelCase_ ( lowercase__ = None ):
'''simple docstring'''
if nums is None or not nums:
raise ValueError("Input sequence should not be empty" )
a_ =n... | 41 | 0 |
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class A ( lowercase_ , unittest.TestCase ):
UpperCamelCase_ : List[Any] =CTRLTokenizer
... | 230 |
'''simple docstring'''
import unittest
from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_te... | 404 | 0 |
"""simple docstring"""
def __lowercase ( a : int , a : float , a : float ) -> str:
return round(float(moles / volume ) * nfactor )
def __lowercase ( a : float , a : float , a : float ) -> Any:
return round(flo... | 704 |
"""simple docstring"""
import argparse
import io
import requests
import torch
from omegaconf import OmegaConf
from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
assign_to_checkpoint,
conv_attn_to_linear,
create_vae_diffusers_config,
ren... | 497 | 0 |
"""simple docstring"""
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
... | 93 |
'''simple docstring'''
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from... | 541 | 0 |
import inspect
import unittest
from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import C... | 647 | def __lowerCamelCase (UpperCAmelCase__ : list[int] ):
if not numbers:
return 0
if not isinstance(UpperCAmelCase__ , (list, tuple) ) or not all(
isinstance(UpperCAmelCase__ , UpperCAmelCase__ ) for number in numbers ):
raise ValueError("numbers... | 647 | 1 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = "▁"
_lowerCAmelCase ... | 10 | from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def _snake_case ( __snake_case , __snake_case ):
return math.sqrt(sum(pow(a - b , 2 ) for a, b in zip(__snake_case , __snake_case ) ) )
def _snake_case ( __snake_cas... | 10 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tensorflow_text_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__SCREAMING_SNAKE_CASE : Any = {
'''configuration_b... | 149 |
import unittest
from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTe... | 149 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
... | 19 |
"""simple docstring"""
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import ... | 19 | 1 |
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils i... | 677 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowerCAmelCase_ ( __snake_case ):
_UpperCamelCase : Tuple = "ClapFeatureExtractor"
_UpperCamelCase : Optional[int] = ("RobertaTokenizer", "RobertaTok... | 677 | 1 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
class A__ ( A__ ):
"""simpl... | 37 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def UpperCamelCase_ ( ) -> int:
a__ : Any = HfArgumentParser(__a )
a__ : Any = parser.parse_args_into_dataclasses()[0]
a__ : Optional[int] = TensorFlowBenchmark(args=__a... | 37 | 1 |
'''simple docstring'''
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
__lowerCamelC... | 418 |
'''simple docstring'''
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
__lowerCamelCase : Union[str, Any] = """\
@article{wang2019superglue,
title={SuperGLUE: A Stickier Benchmark for General-Purpose ... | 418 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
__A = {
"configuration_trocr": ["TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP", "TrOCRConfig"],... | 469 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
"unc-nlp/lxmert-base-uncased": "https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json",
}
class __low... | 469 | 1 |
from __future__ import annotations
from typing import TypedDict
class A ( __lowercase ):
_snake_case =42
_snake_case =42
def a__ ( lowercase__ ):
'''simple docstring'''
if not isinstance(lowercase__ ... | 550 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowercase : Any ={
"""configuration_upernet""": ["""UperNetConfig"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvaila... | 550 | 1 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowerCamelCase : Union[str, Any] = logging.get_logger(__name__)
__lowerCamelCase : List[... | 323 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__lowerCamelCase : Any = {
"configuration_vision_encoder_decoder": ["VisionEncoderDecoderConfig", "VisionEncoderDecode... | 323 | 1 |
import requests
_snake_case : List[str] = "YOUR API KEY"
def lowerCAmelCase_ ( __lowerCamelCase , __lowerCamelCase = giphy_api_key ):
__snake_case : Dict = "+".join(query.split() )
__snake_case : Optional[int] = F'https:/... | 705 |
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import c... | 203 | 0 |
'''simple docstring'''
import re
import time
from typing import Optional
import IPython.display as disp
from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length
def lowerCAmelCase_ ( snake_case_ : List[Any] ) -> int:
'''simp... | 78 | '''simple docstring'''
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand
def _SCREAMING_SNA... | 107 | 0 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
__lowerCAmelCase : Union[str, Any] = logging.ge... | 702 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__lowerCAmelCase : List[str] = {
'configuration_canine': ['CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CanineConfig'],
'tokenization_canine': ['Cani... | 76 | 0 |
import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
SCREAMING_SNAKE_CASE__ : Tuple = HfApi()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {}
# fmt: off
SCREAMING_SNAKE_CASE__ : str = torch.tensor([
... | 85 | import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
SCREAMING_SNAKE_CASE__ : Any ... | 85 | 1 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( __snake_case : int = 60_08_51_47_51_43 ):
'''simple docstring'''
try:
lowercase = int(__snake_case )
except (TypeError, ValueError):
raise TypeError('Parameter n must be int or castable to int.' ... | 134 |
"""simple docstring"""
import baseaa
def _SCREAMING_SNAKE_CASE ( __snake_case : str ):
'''simple docstring'''
return baseaa.baaencode(string.encode('utf-8' ) )
def _SCREAMING_SNAKE_CASE ( __snake_case : bytes ):
'''simple docstring'''
... | 134 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch... | 680 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
__magic_name__ : Tuple = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
excep... | 281 | 0 |
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transform... | 32 |
from math import ceil
from typing import List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging
A_ : List... | 32 | 1 |
from __future__ import annotations
from math import gcd
def A ( lowercase__ : int , lowercase__ : int = 2 , lowercase__ : int = 1 , lowercase__ : int = 3 , ) -> int | None:
# A value less than 2 can cause an infinite loop in the algorithm.
if num < 2:
raise ValueError("""The input ... | 45 |
'''simple docstring'''
from manim import *
class _UpperCamelCase ( SCREAMING_SNAKE_CASE):
'''simple docstring'''
def a__ ( self ) -> List[str]:
lowercase : List[Any] = Rectangle(height=0.5 , width=0.5 )
lowercase : str = ... | 372 | 0 |
def a_ (_lowerCAmelCase : int )-> int:
snake_case: List[Any] = abs(_lowerCAmelCase )
snake_case: Union[str, Any] = 0
while n > 0:
res += n % 10
n //= 10
return res
def a_ (_lowerCAmelCase : int )-> int... | 701 | import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('9.1.0'):
__lowerCAmelCase : List[Any] = {
'linear': PIL.Image.Resampling.BILINEAR,
'bilinear': PIL.Image.Resamp... | 164 | 0 |
'''simple docstring'''
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-... | 689 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import loggi... | 689 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__snake_case : Dict = {
"""configuration_instructblip""": [
"""INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""InstructBlipConfig""",
"""Instruc... | 716 |
import argparse
import os
from pathlib import Path
import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download
from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
BarkCoarseConfig,
... | 365 | 0 |
def lowerCamelCase ( UpperCamelCase : int ) -> "list[int]":
if upper_limit < 0:
raise ValueError('Limit for the Catalan sequence must be ≥ 0' )
_lowerCamelCase = [0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
_lowerCamelCase = 1
if upper_limit > 0... | 544 | import argparse
import math
import os
from copy import deepcopy
import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
A = {
'gwf-440k': {
'url... | 544 | 1 |
'''simple docstring'''
import numpy as np
import torch
import tqdm
from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler
class SCREAMING_SNAKE_CASE( A__ ):... | 528 |
'''simple docstring'''
import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
'''split_dict''' , [
SplitDict(),
SplitDict({'''train''': SplitInfo(name='''train''' , num_bytes=133... | 528 | 1 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece... | 40 |
'''simple docstring'''
import numpy as np
class __a :
def __init__( self : Optional[int] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = (0, 0)
__SCREAMING_SNAKE_CASE = None
__SCREAMING_SNAKE_CASE = 0
__SCR... | 109 | 0 |
'''simple docstring'''
import copy
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
__snake_case : List[str] =... | 716 | '''simple docstring'''
from itertools import product
def _UpperCAmelCase ( _UpperCamelCase : int, _UpperCamelCase : int ) -> list[int]:
A_ = sides_number
A_ = max_face_number * dice_number
A_ = [0] * (max_total + 1)
... | 174 | 0 |
import datasets
from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
SCREAMING_SNAKE_CASE__ : List[Any] = '\\n@INPROCEEDINGS{Papineni02bleu:a,\n author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\n title = {... | 311 | import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def lowercase( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ) -> Any:
... | 537 | 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
enable_full_determi... | 711 |
'''simple docstring'''
import unittest
from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import Tokenize... | 425 | 0 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.